Combinatorial Methods for Chemical and Biological Sensors

Integrated Analytical Systems

Series Editor:Radislav A. PotyrailoGE Global Research CenterNiskayuna, NY

For other titles published in this series, go towww.springer.com/series/7427

Radislav A. Potyrailo • Vladimir M. MirskyEditors

Combinatorial Methods for Chemical and Biological Sensors

EditorsRadislav A. PotyrailoGeneral Electric CompanyGlobal Research CenterNiskayuna, NY [email protected]

Vladimir M. MirskyLausitz University of Applied SciencesNanobiotechnology01968 [email protected]

ISBN: 978-0-387-73712-6 e-ISBN: 978-0-387-73713-3DOI: 10.1007/978-0-387-73713-3

Library of Congress Control Number: 2008940864

© Springer Science+Business Media, LLC 2009All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science + Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden.The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identifi ed as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.

Cover illustration: Richard Oudt

Printed on acid-free paper

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Series Preface

In my career I’ve found that “thinking outside the box” works better if I know what’s “inside the box.”

Dave Grusin, composer and jazz musician

Different people think in different time frames: scientists think in decades, engineers think in years, and investors think in quarters.

Stan Williams, Director of Quantum Science Research,Hewlett Packard Laboratories

Everything can be made smaller, never mind physics;Everything can be made more efficient, never mind thermodynamics;Everything will be more expensive, never mind common sense.

Tomas Hirschfeld, pioneer of industrial spectroscopy

Integrated Analytical Systems

Series Editor: Dr. Radislav A. Potyrailo, GE Global Research, Niskayuna, NY

The book series Integrated Analytical Systems offers the most recent advances in all key aspects of development and applications of modern instrumentation for chemical and biological analysis. The key development aspects include: (i) innova-tions in sample introduction through micro- and nano-fluidic designs, (ii) new types and methods of fabrication of physical transducers and ion detectors, (iii) materials for sensors that became available due to the breakthroughs in biology, combinato-rial materials science and nanotechnology, and (iv) innovative data processing and mining methodologies that provide dramatically reduced rates of false alarms.

A multidisciplinary effort is required to design and build instruments with previ-ously unavailable capabilities for demanding new applications. Instruments with more sensitivity are required today to analyze ultra-trace levels of environmental pollutants, pathogens in water, and low vapor pressure energetic materials in air. Sensor systems with faster response times are desired to monitor transient in-vivo events and bedside patients. More selective instruments are sought to analyze

v

specific proteins in vitro and analyze ambient urban or battlefield air. For these and many other applications, new analytical instrumentation is urgently needed. This book series is intended to be a primary source on both fundamental and practical information of where analytical instrumentation technologies are now and where they are headed in the future.

Looking back over peer-reviewed technical articles from several decades ago, one notices that the overwhelming majority of publications on chemical and biological analysis has been related to chemical and biological sensors and has originated from Departments of Chemistry in universities and Divisions of Life Sciences of governmental laboratories. Since then, the number of disciplines involved in this research field has dramatically increased because of the ever-expanding needs for miniaturization (e.g. for in-vivo cell analysis, embedding into soldier uniforms), lower power consumption (e.g. harvested power), and the ability to operate in complex environments (e.g. whole blood, industrial water, or battle-field air) for more selective, sensitive, and rapid determination of chemical and biological species. Compact analytical systems that have a sensor as one of the system components are becoming more important than individual sensors. Thus, in addition to traditional sensor approaches, a variety of new themes have been introduced to achieve an attractive goal of analyzing chemical and biological spe-cies on the micro- and nano-scale.

vi Series Preface

Foreword

Combinatorial Development of Sensing Materials: Where We Are and Where We Are Going

A significant portion of sensor research involves design of new sensing materials. Early sensing schemes were often based on accidental discoveries and labor-intensive rational optimization of materials. In recent years, strategies have changed greatly because rational design often has been complemented by combinatorial approaches. Combinatorial chemistry, initially developed to synthesize large libraries of organic compounds, has experienced a tremendous growth in many respects. In the pharma-ceutical industry, thousands or tens of thousands of new compounds are being synthesized and tested daily. It was only a question of time until this technology was transferred to materials research, in particular to the chemistry of organic and inor-ganic materials for use in sensors. Now, large numbers of different types of such sensing materials can be produced. Once such libraries are available, methods for high-throughput screening of the respective materials have to be found. In fact, devel-opment of combinatorial screening tools appears to require a sizable time commit-ment and effort because each material is likely to be tested for its response to more than just a single analyte. However, as has already been shown by many research groups worldwide, these investments do have significant and fast pay-offs.

This book reflects the exciting developments that have been made in the areas of combinatorial chemistry and high-throughput screening of sensing materials. I have to congratulate the editors for having compiled this book on this extremely timely subject, and the authors for having contributed state-of-the-art chapters to the various sections. This volume will be an invaluable source of information to all scientists and practitioners working in this field.

Regensburg, Germany Otto S. Wolfbeis

vii

Preface

Sensing materials play a key role in the successful implementation of chemical and biological sensors. The multidimensional nature of the interactions between func-tion and composition, preparation method, and end-use conditions of sensing mate-rials often makes their rational design for real-world applications very challenging. These practical challenges in rational design of sensing materials provide tremen-dous potential for combinatorial and high-throughput research, which is the applied use of technologies and automation for the rapid synthesis and performance screen-ing of relatively large number of compounds. Ideally, from these experiments, rel-evant descriptors are determined to understand the details of materials design and to establish quantitative structure–function relationships. This attractive goal has been reached only for a few sensing materials systems. In other cases, empirical screening and simple data mining are being used.

This book is the compilation and critical analysis of work recently performed in research laboratories around the world on the implementation of combinatorial and high-throughput research methodologies for the discovery and optimization of new sensing materials. The book will be of interest to the reader because of its several innovative aspects. First, it provides a detailed description and analysis of strategies for setting up successful processes for screening of sensing materials for both chemical and biological sensors. Second, it summarizes the advances, remaining challenges and suggests the opportunities in combinatorial research in the areas chemical and biosensors based on polymeric, inorganic, biological, and formulated sensing materials. Third, it provides an insight into how to improve the efficiency of the combinatorial screening of sensing materials and generation of new knowledge by combining combinatorial experimentation and advanced data mining techniques.

This book is intended to be a primary source on both fundamental and practical information of where high-throughput analysis technologies are now and where they are headed in the future. It is addressed to the rapidly growing number of active practitioners and those who are interested in starting research in the field of sensing materials for chemical sensors and biosensors, directors of industrial and government research centers, laboratory supervisors and managers, students and lecturers. Combinatorial and high-throughput materials screening approaches

ix

x Preface

analyzed in this book will be also of interest to researchers from many other fields of experimental science working on materials design. The structure of this book offers a basis for a high-throughput instrumentation course at advanced under-graduate or graduate level.

We thank Ken Howell at Springer for his patience during the implementation of this book project and for encouraging us during the various stages of this project. We express our gratitude to the authors for their efforts in preparing their chapters on time and to the referees for reviewing them. R.A.P. thanks the leadership team at GE Global Research for supporting the whole GE Combinatorial Chemistry effort. V.M.M. is grateful to Prof. O.S. Wolfbeis for initiating the development of combinatorial approaches on discovery and optimization of sensing materials in the University of Regensburg. Last, but not least, we thank our families for their patience and enthusiastic support of this project.

Niskayuna, NY Radislav A. PotyrailoSenftenberg, Germany Vladimir M. Mirsky

Contents

Section 1 Benefi ts of Combinatorial Approaches to Sensor Problems

1 Introduction to Combinatorial Methods for Chemical and Biological Sensors ............................................................................ 3 Radislav A. Potyrailo and Vladimir M. Mirsky

2 Main Concepts of Chemical and Biological Sensing ............................ 25 Marek Trojanowicz

Section 2 Self-Assembled Monolayers and Nanoparticles

3 Self-Assembled Monolayers with Molecular Gradients ...................... 63 Michael Schäferling, Michael Riepl, and Bo Liedberg

4 Combinatorial Libraries of Fluorescent Monolayers on Glass .......... 81 Lourdes Basabe-Desmonts, David N. Reinhoudt,

and Mercedes Crego-Calama

5 High-Throughput Screening of Vapor Selectivity of Multisize CdSe Nanocrystal/Polymer Composite Films ................. 117

Radislav A. Potyrailo and Andrew M. Leach

Section 3 Molecular Imprinting

6 Computational Design of Molecularly Imprinted Polymers ............... 135 Sreenath Subrahmanyam and Sergey A. Piletsky

7 Experimental Combinatorial Methods in Molecular Imprinting ...... 173 Börje Sellergren, Eric Schillinger, and Francesca Lanza

xi

xii Contents

Section 4 Biological Receptors

8 Combinatorially Developed Peptide Receptors for Biosensors .......... 201 Chikashi Nakamura and Jun Miyake

9 Combinatorial Libraries of Arrayable Single-Chain Antibodies ....... 223 Itai Benhar

10 A Modular Strategy for Development of RNA-Based Fluorescent Sensors................................................................................. 249

Masatora Fukuda, Tetsuya Hasegawa, Hironori Hayashi, and Takashi Morii

Section 5 Inorganic Gas-Sensing Materials

11 Impedometric Screening of Gas-Sensitive Inorganic Materials ......... 273 Maike Siemons and Ulrich Simon

12 Design of Selective Gas Sensors Using Combinatorial Solution Deposition of Oxide Semiconductor Films ............................ 295

Jong-Heun Lee, Sun-Jung Kim, and Pyeong-Seok Cho

Section 6 Electrochemical Synthesis of Sensing Materials

13 Combinatorial Development of Chemosensitive Conductive Polymers .............................................................................. 315

Vladimir M. Mirsky

14 Robotic Systems for Combinatorial Electrochemistry ........................ 331 Sabine Borgmann and Wolfgang Schuhmann

Section 7 Optical Sensing Materials

15 Combinatorial Chemistry for Optical Sensing Applications .............. 373 M.E. Díaz-García, G. Pina Luis, and I.A. Rivero-Espejel

16 High Throughput Production and Screening Strategies for Creating Advanced Biomaterials and Chemical Sensors .............. 393

William G. Holthoff, Loraine T. Tan, Ellen L. Shughart, Ellen M. Cardone, and Frank V. Bright

17 Diversity-Oriented Fluorescence Library Approach for Novel Sensor Development ................................................................................ 419

Shenliang Wang and Young-Tae Chang

Contents xiii

18 Construction of a Coumarin Library for Development of Fluorescent Sensors ............................................................................ 441

Tomoya Hirano and Hiroyuki Kagechika

Section 8 Mining of New Knowledge on Sensing Materials

19 Determination of Quantitative Structure–Property Relationships of Solvent Resistance of Polycarbonate Copolymers Using a Resonant Multisensor System ............................ 455

Radislav A. Potyrailo, Ronald J. Wroczynski, Patrick J. McCloskey, and William G. Morris

20 Computational Approaches to Design and Evaluation of Chemical Sensing Materials .............................................................. 471

Margaret A. Ryan and Abhijit V. Shevade

Section 9 Outlook

21 Combinatorial Methods for Chemical and Biological Sensors: Outlook..................................................................................................... 483

Radislav A. Potyrailo and Vladimir M. Mirsky

Index ................................................................................................................. 489

Contributors

Lourdes Basabe-DesmontsDepartment of Supramolecular Chemistry and Technology, MESA+ Institute for Nanotechnology, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands

Itai BenharDepartment of Molecular Microbiology and Biotechnology, The George S. Wise Faculty of Life Sciences, Green Building, Room 202, Tel-Aviv University, Ramat Aviv 69978, [email protected]

Sabine BorgmannTechnische Universität Dortmund, Fachbereich Chemie Biologisch-Chemische Mikrostrukturtechnik, Otto-Hahn Strasse 6, D-44227 Dortmund, [email protected]

Frank V. BrightDepartment of Chemistry, The State University of New York, Buffalo, NY 14260-3000, USA [email protected]

Ellen M. CardoneDepartment of Chemistry, The State University of New York, Buffalo, NY 14260-3000, USA [email protected]

Young-Tae ChangDepartment of Chemistry, and MedChem Program of the Offi ce of Life Sciences, National University of Singapore, Singapore 117543andLaboratory of Bioimaging Probe Development, Singapore Bioimaging Consortium, Agency for Science, Technology and Research (A*STAR), Biopolis, Singapore [email protected]

xv

xvi Contributors

Pyeong-Seok ChoKorea University, Department of Materials Science and Engineering, Seoul 136-713, Korea [email protected]

Mercedes Crego-CalamaDepartment of Supramolecular Chemistry and Technology, MESA+ Institute for Nanotechnology, University of Twente, P.O. Box 217, 7500 AE Enschede, [email protected]

M.E. Díaz-García Department of Physical and Analytical Chemistry, Faculty of Chemistry, University of Oviedo, Av. Julián Clavería 8, 33006, Oviedo, [email protected]

Masatora FukudaInstitute of Advanced Energy, Graduate School of Energy Science, Department of Fundamental Energy Science, Kyoto University Uji, Kyoto 611-0011, Japan

Tetsuya HasegawaInstitute of Advanced Energy, Graduate School of Energy Science, Department of Fundamental Energy Science, Kyoto University Uji, Kyoto 611-0011, Japan

Hironori HayashiInstitute of Advanced Energy, Graduate School of Energy Science, Department of Fundamental Energy Science, Kyoto University Uji, Kyoto 611-0011, Japan

Tomoya HiranoTokyo Medical and Dental University, School of Biomedical Sciences, Tokyo Medical and Dental University, 2-3-10 Kanda-Surugadai, Chiyoda-ku, Tokyo 101-0062, [email protected]

William G. HolthoffJoint Expeditionary Forensics Program, Naval Surface Warfare Center Dahlgren, Asymmetric Operations Technology Branch (Z11), Dahlgren, VA 22448 – 5160, [email protected]

Hiroyuki KagechikaTokyo Medical and Dental University, School of Biomedical Sciences, Tokyo Medical and Dental University 2-3-10 Kanda-Surugadai, Chiyoda-ku, Tokyo 101-0062, [email protected]

Contributors xvii

Sun-Jung KimKorea University, Department of Materials Science and Engineering, Seoul 136-713, Korea [email protected]

Francesca LanzaINFU, University of Dortmund, Otto Hahn Strasse 6, D-44221 Dortmund, Germany

Andrew M. LeachGeneral Electric Company, Global Research Center, Niskayuna, NY 12309, [email protected]

Jong-Heun LeeKorea University, Department of Materials Science and Engineering, Seoul 136-713, Korea [email protected]

Bo LiedbergUniversity of Linköping, Division of Molecular Physics, Department of Physics, Chemistry and Biology, Linköping University, SE-581 83 Linköping, [email protected]

G. Pina LuisDepartment of Physical and Analytical Chemistry, Faculty of Chemistry, University of Oviedo, Av. Julián Clavería 8, 33006, Oviedo, Spain

Patrick J. McCloskeyGeneral Electric Company, Global Research Center, Niskayuna, NY 12309, [email protected]

Vladimir M. MirskyLausitz University of Applied Sciences, Nanobiotechnology, 01968 Senftenberg, [email protected]

Jun MiyakeResearch Institute of Cell Engineering, National Institute of Advanced Industrial Science and Technology, 1-8-31 Midorigaoka, Ikeda 563-8577 Osaka, [email protected]

Takashi MoriiInstitute of Advanced Energy, Graduate School of Energy Science, Department of Fundamental Energy Science, Kyoto University Uji, Kyoto 611-0011, [email protected]

xviii Contributors

William G. MorrisGeneral Electric Company, Global Research Center, Niskayuna, NY 12309, [email protected]

Chikashi Nakamura Research Institute for Cell Engineering, National Institute of Advanced Industrial Science and Technology, AIST Tsukuba Central 6, Tsukuba, Ibaraki, [email protected]

Sergey A. PiletskyCranfi eld Biotechnology Center, Cranfi eld, University at Silsoe, Bedfordshire, MK45 4DT, UKs.piletsky@cranfi eld.ac.uk

Radislav A. Potyrailo General Electric Company, Global Research Center, Niskayuna, NY 12309, [email protected]

David N. Reinhoudt Department of Supramolecular Chemistry and Technology, MESA+ Institute for Nanotechnology, UnivFersity of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands

Michael RieplOlympus Life Science Research Europa GmbH, Sauerbruchstr. 50, D-81377 Munich, [email protected]

I.A. Rivero-EspejelTechnological Institute of Tijuana, P.O. 1166, Tijuana, 22000, Baja California, [email protected]

Margaret A. RyanJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, [email protected]

Michael SchäferlingUniversity of Regensburg, Institute of Analytical Chemistry, Chemo- and Biosensors, D-93040 Regensburg, [email protected]

Contributors xix

Eric SchillingerINFU, University of Dortmund, Otto Hahn Strasse 6, D-44221 Dortmund, Germany

Wolfgang SchuhmannRuhr-University Bochum, Analytical Chemistry - Electroanalysis and Sensors, Universitätsstr. 150, D-44780 Bochum, [email protected]

Börje SellergrenINFU, University of Dortmund, Otto Hahn Strasse 6, D-44221 Dortmund, [email protected]

Abhijit V. ShevadeJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, [email protected]

Ellen L. ShughartU.S. Army Research Laboratory, AMSRD-ARL-SE-EO, Adelphi, MD 20783 – 1138, [email protected]

Maike SiemonsRWTH Aachen University, Institute of Inorganic Chemistry, D-52056 Aachen, [email protected]

Ulrich SimonRWTH Aachen University, Institute of Inorganic Chemistry, D-52056 Aachen, [email protected]

Sreenath SubrahmanyamCranfi eld Biotechnology Center, Cranfi eld University at Silsoe, Bedfordshire, MK45 4DT, UKsri@cranfi eld.ac.uk

Loraine T. TanDepartment of Chemistry, University at Buffalo, The State University of New York, Buffalo, NY 14260-3000, USA [email protected]

xx Contributors

Marek TrojanowiczUniversity of Warsaw, Department of Chemistry, ul. Pasteura 1, 02-093 Warsaw, [email protected]

Shenliang WangDepartment of Chemistry, New York University, NY 10003, [email protected]

Ronald J. WroczynskiGeneral Electric Company, Global Research Center, Niskayuna, NY 12309, [email protected]

Section 1Benefits of Combinatorial Approaches

to Sensor Problems

Chapter 1Introduction to Combinatorial Methods for Chemical and Biological Sensors

Radislav A. Potyrailo and Vladimir M. Mirsky

Abstract Sensing materials play a critical role in advancing selectivity, response speed, and sensitivity of chemical and biological determinations in gases and liquids. The desirable capabilities of sensing materials originate from their numerous functional parameters, which can be tailored to meet specific sensing needs. By increasing the structural and functional complexity of sensing materials, the ability to rationally define the precise requirements that will result in desired materials properties becomes increas-ingly limited. Combinatorial experimentation methodologies impact all areas of sensing materials research including inorganic, organic, and biological sensing materials.

1 Introduction

A modern sensor system incorporates several key components such as a sample delivery unit, sensing material, transducer, and data processor. Developing sensing materials is a recognized challenge because it requires extensive experimentation not only to achieve the best short-term performance, but also long-term stability, manufacturability, cost, and other practical issues. In a sensor device, a sensing material is applied onto a suitable physical transducer to convert a change in a property of a sensing material into a suitable physical signal. The signal obtained from a single transducer or from an array of transducers is further processed to provide useful information about the identity and concentration of species in the sample.

Compared with sensing based on intrinsic analyte properties (e.g., spectroscopic, dielectric, paramagnetic), sensing that utilizes a responsive sensing material1–8 dramati-cally expands the range of detected species, improves sensor performance (for example,

R.A. Potyrailo and V.M. Mirsky (eds.), Combinatorial Methods for Chemical 3and Biological Sensors, DOI: 10.1007/978-0-387-73713-3_1,© Springer Science + Business Media, LLC 2009

R.A. Potyrailo (*)Chemical and Biological Sensing Laboratory, Chemistry Technologies and Material Characterization, General Electric Global Research, Niskayuna, New York, NY 12309, [email protected]

V.M. Mirsky Lausitz University of Applied Sciences, Department of Nanobiotechnology, 01968 Senftenberg, [email protected]

4 R.A. Potyrailo and V.M. Mirsky

analyte detection limits and analysis time), and is more straightforwardly adaptablefor miniaturization.9–29 These attractive features can be offset by some limitations,for example, insufficient selectivity, poisoning, poor long-term stability, dependence of response rate on analyte concentration and long recovery time.

Sensing materials can be categorized into three general groups that include inor-ganic, organic, and biological materials. We define inorganic sensing materials as materials that have inorganic signal-generation components (e.g., metals, metal oxides, semiconductor nanocrystals) that may or may not be further incorporated into a matrix. Organic sensing materials comprise indicator dyes, polymer/reagent compositions, conjugated polymers, and molecularly imprinted polymers. Biological sensing materi-als include receptors such as aptamers, peptides, antibodies, enzymes, etc.

In this chapter, we set a stage with the general principles of combinatorial screen-ing technologies of sensing materials. New parallel synthesis and advanced analytical instruments and data mining tools accelerate discovery and optimization of sensing materials and provide more fundamental knowledge on the material fabrication with tailored initial and the long-term stability properties.

2 Challenges in Rational Design of Sensing Materials

Rational design of sensing materials based on prior knowledge is a very attractive approach because it could avoid time-consuming synthesis and testing of numerous materials candidates.30–32 However, to be quantitatively successful, rational design33–38 requires detailed knowledge regarding relation of the intrinsic properties of sensing materials to their performance properties (e.g., affinity to an analyte and interferences, kinetic constants of analyte binding and dissociation, long-term sta-bility, shelf life, resistance to poisoning, best operation temperature, decrease of reagent performance after immobilization, etc.). This knowledge is often obtained from extensive experimental and simulation data. Some examples of successful results of rational design of sensing materials are presented in Table 1.1.

However, with the increase of structural and functional complexity of materials, the ability to rationally define the precise requirements that result in a desired set of properties becomes increasingly limited.51 Thus, in addition to rational design, a variety of sensing materials ranging from dyes and ionophores, to biopolymers, organic and hybrid polymers, and to nanomaterials have been discovered using detailed experimental observations or simply by chance.52–60 Such an approach in development of sensing materials reflects a more general situation in materials design that is “still too dependent on serendipity” with only limited capability for rational materials design as recently noted by Eberhart and Clougherty.61

Conventionally, the detailed experimentation with sensing materials candidates for their screening and optimization consumes tremendous amount of time and project cost without adding to the “intellectual satisfaction.” Fortunately, new syntheticand measurement principles and instrumentation significantly accelerate the devel-opment of new materials. The practical challenges in rational sensor material design also provide tremendous prospects for combinatorial materials research.

Introduction to Combinatorial Methods for Chemical and Biological Sensors 5

Table 1.1 Examples of rational design of sensing materials

Group of sensing materials Rational design Required knowledge Ref.

Inorganic Wavelength of the plasmon exci-tation in nanoparticles

Physical models for light interac-tion with appropriate particle structure

39

Inorganic Gas sensitive materials based on carbon nanotubes

Doping level of impurity atoms in a nanotube

40

Inorganic Gas response of polycrystalline semiconducting metal oxides

Thermo-mechanical and thermo-chemical effects on percolation-dependent electron transport

41

Inorganic Hydrophobic coating of gold chemoresistor for rejection of polar interferents

Polarity of analyte and interfer-ents, design of self-assembled monolayers

42

Organic General spectral features of dyes for sensing applications

Quantum chemical calculations and empirical data

43

Organic Vapor-sorbing sensing materials Vapor partition coefficients between air and sensing materials

44

Organic Polymer matrix components for molecular imprinting

Empirical data on analyte binding to different polymerizable mono-mers

45

Organic Electrical wiring of chemosensi-tive layers by a conducting polymer

Chemosensitive and conductive properties of conjugated polymers

46

Biological Accuracy of biosensors based on immobilized enzymes

Hydrogen bonding effects between immobilization matrix and enzymes

47

Biological Application of immobilized DNA as a receptor for enro-floxacines

Data on binding of fluoroqinolo-nes with nucleic acids

48

Biological Selectivity and sensitivity of immobilized aptamers

Nature and length of covalent linker for immobilization

49

Biological Biosensors with thermally stable enzyme bioreceptors

Quaternary structure modeling of known tertiary structures of related proteins

50

3 General Principles of Combinatorial Materials Screening

In pharmaceutical and biotechnology industries, combinatorial synthesis and high-throughput analysis methods have found their applications in systematic searching of large parameter spaces for new candidate therapeutic agent molecules.62 Combinatorial chemistry originated in several laboratories around the world where Frank in Germany,63 Geysen in Australia,64 and Houghten in USA65

developed methods to make more compounds in a shorter period of time.66

In materials science, the materials properties depend not only on composition, but also on morphology, microstructure, and other parameters related to the material-preparation conditions and on the end-use environment. As a result of this complexity,


a true combinatorial experimentation is rarely performed in materials science with a complete set of materials and process variables explored. Instead, carefully selected subsets of the parameters are often explored in an automated parallel or rapid sequen-tial fashion using high-throughput experimentation (HTE). Nevertheless, the terms “combinatorial chemistry” and “combinatorial materials science” are often applied for all types of automated parallel and rapid sequential materials and process param-eters evaluation processes. Thus, an adequate definition of combinatorial and high-throughput materials science is a process that couples the capability for parallel production of large arrays of diverse materials together with different high-throughput measurement techniques for various intrinsic and performance properties followed by the navigation in the collected data for identifying “lead” materials.67–75

Individual aspects of accelerated materials development have been known for dec-ades. These include combinatorial and factorial experimental designs,76 parallel synthesis of materials on a single substrate,77,78 screening of materials for performance properties,79

and computer data processing.80,81 However, it took the innovative scientific vision of Joseph Hanak to suggest in 1970 an integrated materials-development workflow.82 Its key aspects included (1) complete compositional mapping of a multicomponent system in one experiment, (2) simple rapid nondestructive all-inclusive chemical analysis, (3) testing of properties by a scanning device, and (4) computer data processing. Hanak was truly ahead of his time and “it took 25 years for the world to realize his idea.”68

In 1995, Xiang, Schultz, and coworkers initiated applications of combinatorial methodologies in materials science with the publication of their paper “A combina-torial approach to materials discovery.”83 Since then, combinatorial materials sci-ence has enjoyed much success, rapid progress for over a decade, and tremendous diversification into a wide variety of types of materials. Besides sensing materials, discussed in this book, examples of materials developed using combinatorial and high-throughput screening techniques include superconductor,83 ferroelectric,84

magnetoresistive,85 luminescent,86 agricultural,87 structural,88 hydrogen storage,89

and organic light-emitting90 materials; ferromagnetic91 and thermoelastic92 shape-memory alloys; heterogeneous,93 homogeneous,94 polymerization,95 and electro-chemical96 catalysts and electrocatalysts for hydrogen evolution97; polymers98;zeolites99; metal alloys100; materials for methanol fuel cells,101 solid oxide fuel cells,102 and solar cells103; automotive,104 waterborne,105 vapor-barrier,106 marine,107

and fouling-release108 coatings.A typical modern combinatorial materials development cycle is outlined in Fig.

1.1. Compared with the initial idea of Hanak,82 the modern workflow has several new important aspects such as planning of experiments, data mining, and scale up. In combinatorial screening of materials, concepts originally thought as highly auto-mated have been recently refined to have more human input, with only an appropri-ate level of automation. For the throughput of 50–100 materials formulations per day, it is acceptable to perform certain aspects of the process manually.109,110 To address numerous materials-specific properties, a variety of high-throughput char-acterization tools are required. Characterization tools are used for rapid and auto-mated assessment of single or multiple properties of the large number of samples fabricated together as a combinatorial array or “library.”71,111,112


In addition to the parallel synthesis and high-throughput characterization instru-mentation that significantly differ from conventional equipment, the data manage-ment approaches are also different from conventional data evaluation.75 In an ideal combinatorial workflow, one should “analyze in a day what is made in a day”113 that requires significant computational assistance. In an exemplary combinatorial work-flow (Fig. 1.1), design and syntheses protocols for materials libraries are computer assisted, materials synthesis and library preparation are carried out with computer-controlled manipulators, and property screening and materials characterization are also software controlled. Further, materials synthesis data as well as property and characterization data are collected into a materials database. This database contains information on starting components, their descriptors, process conditions, materials testing algorithms, and performance properties of libraries of sensing materials. Data in such a database is not just stored, but also processed with the proper statisti-cal analysis, visualization, modeling, and data-mining tools.

Combinatorial synthesis of materials provides a good possibility for formation of banks of combinatorial materials (Fig. 1.1). Such banks can be used, for exam-ple, for further reinvestigation of the materials of interest for some new applications or as reference materials. However, this approach did not find wide applications yet in combinatorial materials science.

New knowledgefor rational

materials design

Bank ofmaterials

Leadmaterials

for scale-up

Fabrication ofmaterials libraries

Measurement ofperformance

Dataanalysis/mining

Design ofexperiments

Databasesystem

Fig. 1.1 Typical cycle in combinatorial and high-throughput approach for materials development


4 Opportunities for Sensing Materials

Development of a sensor system with a new sensing material includes several phases such as discovery with initial observations, feasibility experimentation, and laboratory scale detailed evaluation, followed by the transition to the pilot scale and to commercial manufacturing (Fig. 1.2). At the initial stage, performance of the sensing material is matched with the appropriate transducer for the signal genera-tion. The stage of the laboratory scale evaluation is very labor-intensive because it involves a detailed testing of sensor performance. Some of the aspects of this evalu-ation include optimization of the sensing material composition and morphology, its deposition method, detailed evaluation of response accuracy, stability, precision, selectivity, shelf-life, long-term stability of the response, key noise parameters (e.g., material instability because of temperature, potential poisons), and potential environmental health and safety (EHS) issues with composition and manufacturing of sensing materials. The pilot scale manufacturing focuses on the identification and elimination of manufacturing issues related to the reproducible, high-yield manufacturing of the sensors. During this phase, alpha and beta trials are typically performed. The alpha trials are typically performed by the researchers on an advanced sensor device prototype to identify issues related to sensor operation and functionality. Beta trials are typically performed on the further improved version of the sensor system by the identified group of end-users to seek their feedback on sensor performance, ease of use, failure modes, etc.

It is difficult to predict quantitatively numerous sensor material parameters using rational approaches (Table 1.2). Relevant descriptors must be determined to under-

Pilot scalemanufacturing

Initialobservation

Time

Long termstability

Shelflife

Manu-facturability

EHS

Detailedselectivity

Feasibility experiments:• Sensitivity • Selectivity• Stability

Commercialmanufacturing

Alphatrials

Betatrials

Opportunities forcombinatorial and high-throughput experimentation

Lab scaledetailed evaluation(lab scale production):• Optimization of material composition• Optimization of material deposition• Response accuracy• Response precision• Key noise parameters

Fig. 1.2 Development phases of new sensing materials in sensors and opportunities for combina-torial and high-throughput experimentation


stand the details of sensor materials design and to establish quantitative structure–function relationships. Although applying combinatorial and high-throughput screening tools can accelerate this process, the number of purely serendipitous combinations of sensor materials components is simply too large to handle even with the “ultra-high throughput screening” in a time and cost-effective manner. It is not feasible to synthesize all possible molecules and apply all possible process conditions to characterize materials function. In this case, a methodology that allows the most promising candidates to be short-listed should be applied. In mate-rials science, focused libraries are often designed, produced, and tested in a high-throughput mode for a subset of a truly “combinatorial” space where the initial subset selection is performed using rational and intuitive approaches.

5 Designs of Combinatorial Libraries of Sensing Materials

Variations of individual parameters of sensing materials result in a modification of a number of response parameters. Thus, the goal of combinatorial and high-throughput development of sensing materials is to determine the structure–function relationships in sensing materials. In combinatorial screening, discrete76,83 and gradient77,82,129, 130, 131 arrays (libraries) of sensing materials are employed. A specific type of library layout will depend on the required density of space to be explored, available library-fabrication capabilities, and capabilities of high-throughput char-acterization techniques.

Table 1.2 Parameters relevant to sensor applications that are difficult or impossible to quantita-tively predict and calculate using existing knowledge and rational approaches

Group of sensing materials Parameter Ref.

Inorganic Fundamental effects of volume dopants on base metal oxide materials 114Inorganic Hot spots in surface-enhanced Raman scattering 115Inorganic Room-temperature gas response of metal oxide nanomaterials 116Organic Selectivity of prospective ionophores 3Organic Design of polymers for selective complexation with desired metal

ions117

Organic Collective effects of preparation method of conducting polymers, polymer morphology, properties of the substrate/film interface

24

Organic Surface ratio of different types of molecules in mixed self-assembled monolayers formed in quasi-equilibrium conditions

118

Biological Oligonucleotide sequence in aptamers for specific and sensitive target binding

119

Biological Activity of immobilized bioreceptors 120Biological Selection of coupling reagents for immobilization of antibodies 121Biological Long-term stability of enzyme-containing polymer films 122Biological Affinity properties of antibodies (kinetic association and dissociation

constants, binding constant)123


Discrete or gradient sensor regions are attractive for a variety of different specific applications and can be produced with a variety of tools. Discrete sensor regions are straightforward to produce using a variety of approaches, for example chemical vapor deposition (Fig. 1.3a), pulsed-laser deposition (Fig. 1.3a), dip coating (Fig. 1.3c), spin coating (Fig. 1.3d), electropolymerization (Fig. 1.3e), slurry dispensing (Fig. 1.3f), liquid dispensing (Fig. 1.3g), screen printing (Fig. 1.3h), and some others.

Sensor material optimization can be performed using gradient sensor materials. Spatial gradients in sensing materials can be generated by varying nature and concentration of starting components, processing conditions, thickness, and some others. Gradient sensor regions can be produced using a variety of approaches, for example in situ photopolymerization (Fig. 1.4a), microextrusion (Fig. 1.4b), sol-vent casting (Fig. 1.4c), self-assembly (Fig. 1.4d), temperature-gradient chemical vapor deposition (Fig. 1.4e), thickness-gradient chemical vapor deposition (Fig. 1.4f), 2-D thickness gradient evaporation of two metals (Fig. 1.4g), gradient surface coverage and gradient particle size (Fig. 1.4h), and some others.

Fig. 1.3 Examples of fabrication methods of discrete sensor arrays (a) Chemical vapor deposi-tion: Array of 16 TiO

2 films fabricated at different deposition temperatures. (b) Pulsed-laser

deposition: Array of 16 compositions of the SnO2 base material with different dopants. (c) Dip

coating: A 24-element array of polycarbonate copolymers. (d) Spin coating: A 48-element array of polymeric compositions formulated with fluorescent reagents for optical vapor sensing. (e)Electropolymerization: A 96-element interdigital electrode array with electropolymerized con-ductive polymers. (f) Robotic slurry dispensing: An array of 64 WO

3 sensing films with differ-

ent bulk- and surface-dopants. (g) Liquid dispensing: A 48-element array of formulated solid polymer electrolyte compositions deposited onto radio-frequency identification sensors. (h)Screen printing: A 54-element array of polymeric formulated films for optical ion sensing. (a)Reprinted with permission from Taylor and Semancik132. (b) Reprinted with permission from Aronova et al133. Copyright 2003 American Institute of Physics. (c) Reprinted with permission from Potyrailo.134 Copyright 2004 Wiley-VCH Publishers. (d) Reprinted with permission from Amis135. (f) Reprinted with permission from Scheidtmann et al136. Copyright 2005 Institute of Physics Publishing. (h) Reprinted with permission from Potyrailo et al137. Copyright 2007 Optical Society of America


Fig. 1.4 Examples of fabrication methods of gradient sensor arrays: (a) In situ photopolymeriza-tion: The left stripe is a polymerized gradient array of siloxanes 1 and 2, beginning at the base with siloxane 1 alone and ending at a final monomer ratio of siloxanes 1 and 2. The right stripe is siloxane 1 as control; (b) Microextrusion: A coiled 1-D polymer fiber array with a gradient concentration of a temperature-sensitive phosphor along its length; (c) Solvent casting: Ternary combination of gradi-ent concentrations of fluorescent reagents in a formulated optical sensor film; (d) Self-assembly: A colloidal crystal sensing film of gradient thickness; (e) Temperature-gradient chemical vapor deposition. A range of microstructures was produced in a metal oxide film; (f) Thickness-gradient chemical vapor deposition. Variable-thickness SiO

2 membrane on top of a metal oxide sensing film

in combination with a temperature gradient sensor operation; (g) 2-D thickness gradient evaporation. A two metal combination with variable thickness of each metal along two orthogonal directions; (h) 2-D immersion: Gradient surface coverage and gradient nanoparticle size along two orthogonal directions in SERS array. (a) Reprinted with permission from Dickinson et al138. Copyright 1997 American Chemical Society. (b) Reprinted with permission from Potyrailo and Wroczynski139.Copyright 2005 American Institute of Physics. (e) Reprinted with permission from Taylor and Semancik132. (f) Reprinted with permission from Sysoev et al140. Copyright 2004 Molecular Diversity Preservation International. (g) Reprinted with permission from Klingvall et al141. Copyright 2005 The Institute of Electrical and Electronics Engineers, Inc. (h) Reprinted with permission from Baker et al142. Copyright 1996 American Chemical Society


Once the gradient or discrete sensing materials array is fabricated, it is exposed to an environment of interest and steady-state or dynamic measurements are acquired. Serial scanning mode of analysis (e.g., optical or impedance spectroscopies)is often performed to provide more detailed information about materials property over parallel analysis (e.g., imaging). When monitoring a dynamic process (e.g., response/recovery time and aging) of sensor materials arranged in an array with a scanning system, the maximum number of elements in sensor library that can be measured with the required temporal resolution can be limited by the data-acquisitionability of the scanning system.143

6 Diversity in Needs for Combinatorial Development of Sensing Materials

An appropriate sensing material is based on a successful combination of two somewhat contradicting major material requirements. The first requirement is to have desired material response to the changes in the concentration of species of interest in a sample. It is desirable for the most applications that the response is fast and selective; therefore, the kinetic association constant and the binding constant (the ratio of the kinetic associa-tion and kinetic dissociation constants) should be high. The second requirement is to have a reversible sensor response; therefore, the kinetic dissociation constant should be also high. Thus, development of sensing materials needs knowledge on how to fulfill these controversial requirements or to find a suitable compromise.

For diverse applications, specifications on sensing materials are often weighted differently according to an application. High reliability, adequate long-term stabil-ity, and resolution top the priority list for industrial sensor users, while often the size and maturity of the technology are the least important factors.2,11,144 The low false positive rate is very critical for the first responders.145 In contrast, medical users focus on cost for disposable sensors. Specific requirements for medical in vivo sensors include blood compatibility and minute size.146 Resistance to gamma radiation during sterilization, the drift-free performance, and cost are the most criti-cal specific requirements for sensors in disposable bioprocess components.147 The importance of continuous monitoring also differs from application to application. For instance, glucose sensing should be performed 2–4 times a day using home blood glucose biosensors, while blood-gas sensors for use in intensive care should be capable of continuous monitoring with subsecond time resolution.148,149

Table 1.3 provides a summary of parameters that should be optimized and controlled for successful development of inorganic, organic, and biological sensing materials. Inorganic sensing materials include catalytic metals for field-effect devices,141,150,151

metal oxides for conductometric7,114,132,133,140,152–164 and cataluminescent165,166 sensors, plasmonic,142,167–174 and semiconductor nanocrystal175–177 materials. Organic sensing materials include indicator dyes (free, polymer immobilized, and surface-confined),178–192

polymeric compositions,124,125,138,193–195 homo and copolymers,196–202 conjugated poly-mers,203–207 and molecularly-imprinted polymers.118,208–211 Biological materials include surface and matrix-immobilized bioreceptors.120,203,212 At present, to find the most appropriate sensing materials, combinatorial methods have become widely accepted..


Table 1.3 Parameters that should be optimized and controlled for successful development of inorganic, organic, and biological sensing materials

Group of sensing materials Type of sensing material Optimized and controlled material parameters

Inorganic Catalytic metals Surface additivesPorosityLayered structureGrain sizeAlloyingDeposition method

Inorganic Metal oxide materials Base single or mixed metal oxidesDeposition method and conditions of base

metal oxide(s)Annealing method and conditionsDopant(s)Doping method and conditionsPurity of materials

Inorganic Plasmonic nanostructures and nanoparticles

Substrate typeNanoparticle materialNanoparticle shape, size, morphologyNanoparticles arrangementSurface functionality

Inorganic Plasmonic nanoparticles in polymers

Size of nanoparticleStrength of polymer/particle interactionPolymer grafting densityPolymer chain length

Organic Indicators Binding constantpH-influenceRedox stateSelectivityToxicityPoisoning agents

Organic Polymeric compositions Analyte-responsive reagentPolymer matrixAnalyte-specific ligandPlasticizerOther agents (stabilizing, phase

transfer, etc.)Common solvent

Organic Conjugated polymers Polymerization conditionsTypes of heterocyclesAdditive(s)Side groupsDopantOxidation stateElectrode materialThicknessMorphology

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